Did Twitter’s Removal of Government and
State-Affiliated Media Labels Empower State Actors?

Allison Koh

COMPTEXT
May 13, 2023

#TBT

“Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda. We believe that people have the right to know when a media account is affiliated directly or indirectly with a state actor” (Twitter, 2020).

In the before-times, Twitter had a Trust and Safety team that had a very extensive system for increasing transparency for users and content moderation.

Adding labels flagging misinformation and potential political biases to tweets was part of their efforts to increase transparency on the platform, and their rationale for applying this to state actors was that “Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda”, as shown in this quote here.

Take the tweet shown here, for example: The headline reads “US warns China of ‘severe’ consequences for helping Russia”. The tweet is tagged with a label that indicates the account publishing it is “Iran state-affiliated media”. While some people might know without the label that Press TV is an Iranian state-affiliated media outlet, not everyone will be aware of this at face value. Without the label, it might be assumed that the US is taking more of a hawkish stance towards China, while the label tells us that this news is being published by an outlet that is funded by a government that is friendly to China and Russia.

Since Elon Musk’s takeover of the platform, Twitter has been undoing a lot of these efforts to add context to information circulated on the platform.

Twitter’s Policy Changes in April 2023

**March 29:** ⬆️ engagement with prominent state media from China, Russia, Iran (Kann 2023)

**April 6:** ~~“In the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their Tweets with these labels to people.”~~

**April 12:** NPR quits Twitter after being labeled as “state-affiliated media” (Folkenflik 2023)

**April 21:** Twitter removes all labels for “government” and “state-affiliated media”

Many of Twitter’s policy changes concerning state actors rapidly escalated in the last month.

On March 29th, The Atlantic Council’s Digital Forensics Lab identified increased engagement in terms of follows and likes, which signified a change in Twitter’s policy that stated “in the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their tweets with these labels to people”. Shown here: Average Twitter followers for Russia Today. On March 28th, when X-axes Jan 1 2023 -> sometime mid-April 2023

On April 6th, this clause was officially removed from Twitter’s Platform Use Guidelines.

Throughout April, Twitter started flagging outlets from the US, UK, and Canada as “state-affiliated” or “government-funded” media, going against their policy to not label public broadcasters that are editorially independent. This move essentially obscured the original intent for the labels to serve as a flag for politically-motivated content, and led NPR to quit the platform on April 12th.

After all this controversy over profile labels and whether public broadcasters from democracies with media freedom should be tagged with similar labels as state-affiliated media that follow the directive of their governments, Twitter removed all labels for “government” and “state-affiliated media” altogether.

Overarching Research Questions and Definitions

After Twitter’s removal of profile labels on April 21st, 2023…

…did engagement with state actors increase?
…did state actors change how they use the platform?

🔎📕 Definitions (Schafer 2019; Twitter 2020)

State actor: Any individual or entity connected to government or state-affiliated media
Government official: Key individuals/entities representing “voices of the nation state abroad”
State-affiliated media: Outlets where the state exercises control over editorial content

Hypotheses and Plans for Exploratory Analysis

Hypotheses 🔎

After Twitter officially removed labels from state actors’ accounts…

H1: …engagement (RTs/likes) ⬆️
H2: …the online activity of state actors changed.
- Tweet volume ⬆️
- Focus (coordination of messaging) ⬆️
Sub-hypotheses on whether these effects are more pronounced for state-affiliated media compared to government official/diplomat accounts.
- Government accounts are official voices of the state
- Plausible deniability regarding editorial independence for state-affiliated media

RQs for Exploratory Analysis 🌏

[How] does engagement with state actors vary across countries?
[How] does the online activity of state actors vary across countries?

Data

State actor accounts: Alliance for Securing Democracy’s Hamilton 2.0 Dashboard
- 1,177 accounts linked to Chinese, Iranian, and Russian state actors
- Proxy for accounts that had government and state-affiliated media labels
- Verifying (some) labels via digital archives
Tweets: Twitter Academic API via twint CLI
- Tweet timeline endpoint; limit to 3,200 tweets per account
- Collected data from 1,038 accounts
  - ~75% government/diplomat accounts
  - ~25% state-affiliated media

Method

Dependent variables 📈

H1: Engagement with state actors ⬆️
- DV1: Retweet volume
- DV2: Like volume
H2: Online activity of state actors changes
- DV3: Tweet volume
- DV4: Focus ✨
Modeling strategies TBD contingent on the data available

Measuring focus 👀

Focus measured as a function of topic diversity: % of unique words used for all topics (Dieng, Ruiz, and Blei 2020)
⬆️ focus/⬇️ topic diversity
Process
1. Translate tweets to English
2. Run topic model(s)
3. Measure topic diversity

Next Steps

Identifying potential data issues

Granularity of data ❓
Some accounts (automatically?) delete tweets that are “too old”

Thank you!

koh@hertie-school.org

https://allisonkoh.github.io/

@allisonwkoh@mastodon.social🎓

@allisonwkoh@fosstodon.org📊

@allisonkoh_

Appendix

References

Dieng, Adji B., Francisco J. R. Ruiz, and David M. Blei. 2020. “Topic Modeling in Embedding Spaces.” Transactions of the Association for Computational Linguistics 8 (December): 439–53. https://doi.org/10.1162/tacl_a_00325.

Folkenflik, David. 2023. “NPR Quits Twitter After Being Falsely Labeled as ’State-Affiliated Media’.” NPR, April.

Kann, Alyssa. 2023. “State-Controlled Media Experience Sudden Twitter Gains After Unannounced Platform Policy Change.” DFRLab.

Schafer, Bret. 2019. “Hamilton 2.0 Methodology & FAQs.” Alliance For Securing Democracy.

Twitter. 2020. “New Labels for Government and State-Affiliated Media Accounts.” https://blog.twitter.com/en_us/topics/product/2020/new-labels-for-government-and-state-affiliated-media-accounts.